Question

我在Python中使用OpenCV只能识别图像上显示的Leaf。我已经能够对我的图像进行分割，现在我正处于＆＃34;在检测到所有这些图像后如何裁剪最大的组件。以下是代码，请看一下。

使用scipy.ndimage，找到组件后我无法前进：

def undesired_objects ( image ):
    components, n = ndimage.label( image )
    components = skimage.morphology.remove_small_objects( components, min_size = 50 )
    components, n = ndimage.label( components )
    plot.imshow( components )
    plot.show()

使用OpenCV connectedComponentsWithStats：

def undesired_objects ( image ):
    image = image.astype( 'uint8' )
    nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
    sizes = stats[1:, -1]; nb_components = nb_components - 1
    min_size = 150
    img2 = np.zeros(( output.shape ))
    for i in range(0, nb_components):
        if sizes[i] >= min_size:
            img2[output == i + 1] = 255
            plot.imshow( img2 )
            plot.show()

然而，在这两种方法中，我仍然得到多个组件。下面，您将找到二进制图像：

Answer 1

我会用这样的代码替换你的代码：

def undesired_objects (image):
    image = image.astype('uint8')
    nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
    sizes = stats[:, -1]

    max_label = 1
    max_size = sizes[1]
    for i in range(2, nb_components):
        if sizes[i] > max_size:
            max_label = i
            max_size = sizes[i]

    img2 = np.zeros(output.shape)
    img2[output == max_label] = 255
    cv2.imshow("Biggest component", img2)
    cv2.waitKey()

组件上的循环现在找到具有最大区域的组件，并在循环结束时显示它。

告诉我这是否适合你，因为我自己没有测试过。

Answer 2

使用cv2.CC_STAT_AREA来提高可读性：

# Connected components with stats.
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)

# Find the largest non background component.
# Note: range() starts from 1 since 0 is the background label.
max_label, max_size = max([(i, stats[i, cv2.CC_STAT_AREA]) for i in range(1, nb_components)], key=lambda x: x[1])

更多内容：https://stackoverflow.com/a/35854198/650885

如何使用OpenCV和Python提取最大的连接组件？

2 个答案: